Course introduction
Piotr Pasza Storożenko@Appsilon
Piotr Pasza Storożenko, Machine Learning Engineer
A bit:
you can find more on my blog: pstorozenko.github.io.
Through the years I got a lot of knowledge about python, R, julia, data science, machine learning, deep learningu, softer developemnt.
Now I can share it with you!
Someone who simuntinesly:
Bussiness talks about AI, experts preffer to say ML…
numpy - working numberspandas - working with data framesmatplotlib and plotly - plotting datascikit-learn - introduction to machine learningstreamlit, quarto, fastapi - sharing your workAnaconda is a standard when it comes to managing python environemnts in data science/machine learning community. It allows to obtain a consistent environment among various systems.
Why is it that important?
Data scienctists often work on many projects at the same time. Each project might require different environment, with specific versions of python and other libraries.
This might be also a relief when working on different projects during studying!
After installing anaconda, you have to clone this course repo, and move yourself in terminal into course repo. Then run:
conda create -n appsilon-ds-course python=3.10 -y
conda activate appsilon-ds-course
pip install -r requirements.txt
If you received the following error message:
ERROR: Could not open requirements file: [Errno 2] No such file or directory: 'requirements.txt'
you’re in a wrong directory.
In case of problems, check out the official conda tutorial.
Why VS Code?
conda environmentspython, R, julia, javascript, typescript etc.Install extensions
Environment for work and studing is ready!
pip vs conda vs pipenv vs …We need multiple environments on a single machine.
How to live, what to use?
NEVER PLAY WITH DATA SCIENCE ON YOUR DEFAULT SYSTEM’S PYTHON
pip + virtualenvpip freezeconda3.9/3.10) and minor (3.10.3/3.10.4) python versionsR or CUDA drivers)conda list --exportpipenvpip + virtualenv plus different python versionsThe most reliable setup for experimenting is to do:
conda create -n my-env python==3.10.4
conda activate my-env
pip install ...
If you need to install CUDA drivers then do it on environment creation conda create -n my-env python cudatoolkit.
After you install all packages, save in README file python version, e.g.,
Project created with python 3.10.4.
and store installed packages with pip freeze > requirements.txt.
Remember that not every package version is available for every python version. For example Tensorflow 2.10 is supported only in python>=3.10.